




'''simple docstring'''


from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


UpperCAmelCase_					=   logging.get_logger(__name__)  # pylint: disable=invalid-name





class  __lowercase    (	__magic_name__			):







   def __init__(						self   ,				UpperCamelCase   ,				UpperCamelCase   )					->     Optional[Any]:
     super().__init__()
     self.register_modules(unet=UpperCamelCase   ,				scheduler=UpperCamelCase   )







   @torch.no_grad()
   def __call__(						self   ,				UpperCamelCase = 1   ,				UpperCamelCase = 100   ,				UpperCamelCase = None   ,				UpperCamelCase = None   ,				UpperCamelCase = True   ,				)					->     Union[AudioPipelineOutput, Tuple]:

     if audio_length_in_s is None:
       __a								=       self.unet.config.sample_size / self.unet.config.sample_rate

     __a								=       audio_length_in_s * self.unet.config.sample_rate

     __a								=       2 ** len(self.unet.up_blocks   )
     if sample_size < 3 * down_scale_factor:
       raise ValueError(
           f"{audio_length_in_s} is too small. Make sure it's bigger or equal to"
           f" {3 * down_scale_factor / self.unet.config.sample_rate}."   )

     __a								=       int(UpperCamelCase   )
     if sample_size % down_scale_factor != 0:
       __a								=       (
           (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
       ) * down_scale_factor
       logger.info(
           f"{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"
           f" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"
           ' process.'   )
     __a								=       int(UpperCamelCase   )

     __a								=       next(iter(self.unet.parameters()   )   ).dtype
     __a								=       (batch_size, self.unet.config.in_channels, sample_size)
     if isinstance(UpperCamelCase   ,				UpperCamelCase   ) and len(UpperCamelCase   ) != batch_size:
       raise ValueError(
           f"You have passed a list of generators of length {len(UpperCamelCase   )}, but requested an effective batch"
           f" size of {batch_size}. Make sure the batch size matches the length of the generators."   )

     __a								=       randn_tensor(UpperCamelCase   ,				generator=UpperCamelCase   ,				device=self.device   ,				dtype=UpperCamelCase   )

     # set step values
     self.scheduler.set_timesteps(UpperCamelCase   ,				device=audio.device   )
     __a								=       self.scheduler.timesteps.to(UpperCamelCase   )

     for t in self.progress_bar(self.scheduler.timesteps   ):
       # 1. predict noise model_output
       __a								=       self.unet(UpperCamelCase   ,				UpperCamelCase   ).sample

       # 2. compute previous image: x_t -> t_t-1
       __a								=       self.scheduler.step(UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ).prev_sample

     __a								=       audio.clamp(-1   ,				1   ).float().cpu().numpy()

     __a								=       audio[:, :, :original_sample_size]

     if not return_dict:
       return (audio,)

     return AudioPipelineOutput(audios=UpperCamelCase   )









'''simple docstring'''


import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
UpperCAmelCase_					=   logging.getLogger()


def     SCREAMING_SNAKE_CASE							(   a_						:				Path					,       a_						:				list      ):
  __a								=       '\n'.join(a_      )
  Path(a_      ).open('w'      ).writelines(a_      )


UpperCAmelCase_					=   "patrickvonplaten/t5-tiny-random"
UpperCAmelCase_					=   "sshleifer/bart-tiny-random"
UpperCAmelCase_					=   "sshleifer/tiny-mbart"

UpperCAmelCase_					=   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks





class  __lowercase    (	__magic_name__			):







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Dict:
     __a								=       Path(self.get_auto_remove_tmp_dir()   ) / 'utest_input.source'
     __a								=       input_file_name.parent / 'utest_output.txt'
     assert not output_file_name.exists()
     __a								=       [' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
     _dump_articles(UpperCamelCase   ,				UpperCamelCase   )

     __a								=       str(Path(self.get_auto_remove_tmp_dir()   ) / 'scores.json'   )
     __a								=       'translation_en_to_de' if model == T5_TINY else 'summarization'
     __a								=       f"\n            run_eval_search.py\n            {model}\n            {input_file_name}\n            {output_file_name}\n            --score_path {score_path}\n            --task {task}\n            --num_beams 2\n            --length_penalty 2.0\n            ".split()

     with patch.object(UpperCamelCase   ,				'argv'   ,				UpperCamelCase   ):
       run_generate()
       assert Path(UpperCamelCase   ).exists()
       # os.remove(Path(output_file_name))







   def 		UpperCamelCase__      (						self   )					->     int:
     self.run_eval_tester(UpperCamelCase   )







   @parameterized.expand([BART_TINY, MBART_TINY]   )
   @slow
   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Union[str, Any]:
     self.run_eval_tester(UpperCamelCase   )







   @parameterized.expand([T5_TINY, MBART_TINY]   )
   @slow
   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Optional[Any]:
     __a								=       Path(self.get_auto_remove_tmp_dir()   ) / 'utest_input.source'
     __a								=       input_file_name.parent / 'utest_output.txt'
     assert not output_file_name.exists()

     __a								=       {
         'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
         'de': [
             'Maschinelles Lernen ist großartig, oder?',
             'Ich esse gerne Bananen',
             'Morgen ist wieder ein toller Tag!',
         ],
     }

     __a								=       Path(self.get_auto_remove_tmp_dir()   )
     __a								=       str(tmp_dir / 'scores.json'   )
     __a								=       str(tmp_dir / 'val.target'   )
     _dump_articles(UpperCamelCase   ,				text['en']   )
     _dump_articles(UpperCamelCase   ,				text['de']   )
     __a								=       'translation_en_to_de' if model == T5_TINY else 'summarization'
     __a								=       f"\n            run_eval_search.py\n            {model}\n            {str(UpperCamelCase   )}\n            {str(UpperCamelCase   )}\n            --score_path {score_path}\n            --reference_path {reference_path}\n            --task {task}\n            ".split()
     testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0']   )

     with patch.object(UpperCamelCase   ,				'argv'   ,				UpperCamelCase   ):
       with CaptureStdout() as cs:
         run_search()
       __a								=       [' num_beams | length_penalty', model, 'Best score args']
       __a								=       ['Info']
       if "translation" in task:
         expected_strings.append('bleu'   )
       else:
         expected_strings.extend(UpperCamelCase   )
       for w in expected_strings:
         assert w in cs.out
       for w in un_expected_strings:
         assert w not in cs.out
       assert Path(UpperCamelCase   ).exists()
       os.remove(Path(UpperCamelCase   )   )



